Análisis de modelos SOM - Frecuencia datos de entrada: dia

Modelo

• ID: 75
• Descripción:
• Frecuencia: dia
• Variables: fecha_cnt, tmax, tmin, precip
• Dimensiones del mapa: 10,10
• Iteraciones: 1000
• Parámetros adicionales:

source("../../lib/som-utils.R")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

source("../../lib/maps-utils.R")

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

Carga del modelo desde disco

mpr.set_base_path_analysis()
model <- mpr.load_model("som-075.rds.xz")
summary(model)

SOM of size 10x10 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 1065343 objects.
Mean distance to the closest unit in the map: 0.167.

plot(model, type="changes")

Carga del dataset de entrada

df <- mpr.load_data("datos_dia_2k.csv.xz")

df

summary(df)

 id_estacion           fecha             fecha_cnt          tmax       
 Length:1065343     Length:1065343     Min.   :  1.0   Min.   :-196.0  
 Class :character   Class :character   1st Qu.: 91.0   1st Qu.: 144.0  
 Mode  :character   Mode  :character   Median :183.0   Median : 201.0  
                                       Mean   :182.8   Mean   : 201.5  
                                       3rd Qu.:274.0   3rd Qu.: 263.0  
                                       Max.   :366.0   Max.   : 469.0  
      tmin             precip            nevada    prof_nieve       
 Min.   :-252.00   Min.   :   0.00   Min.   :0   Min.   :   0.0000  
 1st Qu.:  47.00   1st Qu.:   0.00   1st Qu.:0   1st Qu.:   0.0000  
 Median : 100.00   Median :   0.00   Median :0   Median :   0.0000  
 Mean   :  97.54   Mean   :  17.05   Mean   :0   Mean   :   0.6185  
 3rd Qu.: 153.00   3rd Qu.:   2.00   3rd Qu.:0   3rd Qu.:   0.0000  
 Max.   : 332.00   Max.   :3361.00   Max.   :0   Max.   :1240.0000  
    longitud        latitud           altitud    
 Min.   :27.82   Min.   :-17.889   Min.   :   1  
 1st Qu.:39.47   1st Qu.: -4.850   1st Qu.:  47  
 Median :41.29   Median : -1.411   Median : 287  
 Mean   :40.10   Mean   : -2.391   Mean   : 486  
 3rd Qu.:42.22   3rd Qu.:  1.296   3rd Qu.: 691  
 Max.   :43.57   Max.   :  4.216   Max.   :2535  

Carga de los mapas

world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")

Mapa de densidad

plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)

Número de elementos en cada celda:

nb <- table(model$unit.classif)
print(nb)

    1     2     3     4     5     6     7     8     9    10    11    12    13 
 5837  1898  2347   914    51   597  1174  2458  7359  2123  8484 12594  7751 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
 1433   211   760  3859  2860  9196  6361 14370  6363  3532  2636  1610  1474 
   27    28    29    30    31    32    33    34    35    36    37    38    39 
10754 13153 13247  9156 14973 18632  8817  5851  7785  5225  4687 10017  5498 
   40    41    42    43    44    45    46    47    48    49    50    51    52 
 9328 14163 15271 10574 12524 10682  6527  5699 14728 10724 16696 14714 16477 
   53    54    55    56    57    58    59    60    61    62    63    64    65 
17842  8833 16763 17443 16799 12374 10523 16757  7997 13117  8982 18640 12487 
   66    67    68    69    70    71    72    73    74    75    76    77    78 
11127 10414 14251 14932  6983 15599 16394 13106 16017 11067 12391 15987 16597 
   79    80    81    82    83    84    85    86    87    88    89    90    91 
10163  4004 18063 18797 18283 12544 12668 17777 16330 11025 13495 12002 20082 
   92    93    94    95    96    97    98    99   100 
15907 17914 12853 14530 19145 17069 11972 12864 10280 

Comprobación de nodos vacíos:

dim_model <- 10*10;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
  print(paste("[Warning] Existen nodos vacíos: ", len_nb, "/", dim_model))
}

Mapa de distancia entre vecinos

plot(model, type="dist.neighbours", shape = "straight")

Influencia de las variables

model_colnames = c("fecha_cnt", "tmax", "tmin", "precip")
model_ncol = length(model_colnames)

Mapa de variables.

plot(model, shape = "straight")

Mapa de calor por variable

par(mfrow=c(3,4))
for (j in 1:model_ncol) {
  plot(model, type="property", property=getCodes(model,1)[,j],
    palette.name=mpr.coolBlueHotRed,
    main=model_colnames[j],
    cex=0.5, shape = "straight")
}

Correlación para cada columna del vector de nodos

if (!empty_nodes) {
  cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
  print(cor)
}

       fecha_cnt       tmax       tmin      precip
[1,] -0.89840637 -0.3100110 -0.3750626  0.06451007
[2,] -0.09815587  0.7976878  0.8060144 -0.32102373

Representación de cada variable en un mapa de factores:

if (!empty_nodes) {
  par(mfrow=c(1,1))
  plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
  lines(c(-1,1),c(0,0))
  lines(c(0,0),c(-1,1))
  text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
  symbols(0,0,circles=1,inches=F,add=T)
}

Importancia de cada variable - varianza ponderada por el tamaño de la celda:

if (!empty_nodes) {
  sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
     {m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
     effectif=nb))
  print(sort(sigma2,decreasing=T))
}

   precip fecha_cnt      tmax      tmin 
0.9819504 0.9794679 0.9790433 0.9759996 

Clustering

if (!empty_nodes) {
  hac <- mpr.hac(model, nb)
}

Visualización de 3 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=3)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=3)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip      
 Min.   :  1.0   Min.   :-196.0   Min.   :-252.00   Min.   :  0.00  
 1st Qu.: 91.0   1st Qu.: 145.0   1st Qu.:  47.00   1st Qu.:  0.00  
 Median :183.0   Median : 201.0   Median : 100.00   Median :  0.00  
 Mean   :182.7   Mean   : 201.7   Mean   :  97.58   Mean   : 15.35  
 3rd Qu.:274.0   3rd Qu.: 263.0   3rd Qu.: 153.00   3rd Qu.:  1.00  
 Max.   :366.0   Max.   : 469.0   Max.   : 332.00   Max.   :548.00  
     nevada    prof_nieve           longitud        latitud       
 Min.   :0   Min.   :   0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.0000   1st Qu.:39.47   1st Qu.: -4.850  
 Median :0   Median :   0.0000   Median :41.29   Median : -1.411  
 Mean   :0   Mean   :   0.6126   Mean   :40.10   Mean   : -2.394  
 3rd Qu.:0   3rd Qu.:   0.0000   3rd Qu.:42.22   3rd Qu.:  1.296  
 Max.   :0   Max.   :1240.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  47.0  
 Median : 287.0  
 Mean   : 485.5  
 3rd Qu.: 690.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip      
 Min.   :  1.00   Min.   :-84.0   Min.   :-133.00   Min.   : 453.0  
 1st Qu.: 88.25   1st Qu.: 89.0   1st Qu.:  42.25   1st Qu.: 572.0  
 Median :253.00   Median :138.0   Median :  92.00   Median : 652.0  
 Mean   :202.57   Mean   :135.2   Mean   :  81.52   Mean   : 710.4  
 3rd Qu.:305.00   3rd Qu.:184.0   3rd Qu.: 130.00   3rd Qu.: 791.0  
 Max.   :366.00   Max.   :344.0   Max.   : 240.00   Max.   :1500.0  
     nevada    prof_nieve         longitud        latitud        
 Min.   :0   Min.   :  0.000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.000   1st Qu.:40.82   1st Qu.: -2.3308  
 Median :0   Median :  0.000   Median :41.77   Median :  0.8856  
 Mean   :0   Mean   :  3.178   Mean   :40.96   Mean   : -0.8443  
 3rd Qu.:0   3rd Qu.:  0.000   3rd Qu.:42.38   3rd Qu.:  1.9756  
 Max.   :0   Max.   :820.000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  58.0  
 Median : 261.0  
 Mean   : 667.5  
 3rd Qu.: 953.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 21.0   Min.   : 17.0   Min.   :-41.00   Min.   :1528   Min.   :0  
 1st Qu.: 86.0   1st Qu.: 81.5   1st Qu.: 47.00   1st Qu.:1576   1st Qu.:0  
 Median :286.0   Median :126.0   Median : 90.00   Median :1741   Median :0  
 Mean   :219.8   Mean   :136.5   Mean   : 91.35   Mean   :1820   Mean   :0  
 3rd Qu.:306.0   3rd Qu.:194.5   3rd Qu.:146.50   3rd Qu.:2036   3rd Qu.:0  
 Max.   :333.0   Max.   :252.0   Max.   :192.00   Max.   :3361   Max.   :0  
   prof_nieve    longitud        latitud            altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:0    1st Qu.:39.48   1st Qu.:  0.3181   1st Qu.:  69.0  
 Median :0    Median :40.80   Median :  0.4731   Median : 333.0  
 Mean   :0    Mean   :39.48   Mean   : -1.2462   Mean   : 627.3  
 3rd Qu.:0    3rd Qu.:41.72   3rd Qu.:  1.6841   3rd Qu.:1055.0  
 Max.   :0    Max.   :43.36   Max.   :  2.4378   Max.   :2371.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

Visualización de 4 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=4)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=4)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip          nevada 
 Min.   :  1.0   Min.   :-196.0   Min.   :-252.00   Min.   :  0.0   Min.   :0  
 1st Qu.: 91.0   1st Qu.: 145.0   1st Qu.:  47.00   1st Qu.:  0.0   1st Qu.:0  
 Median :182.0   Median : 202.0   Median : 100.00   Median :  0.0   Median :0  
 Mean   :182.4   Mean   : 202.5   Mean   :  97.78   Mean   : 11.1   Mean   :0  
 3rd Qu.:273.0   3rd Qu.: 264.0   3rd Qu.: 153.00   3rd Qu.:  1.0   3rd Qu.:0  
 Max.   :366.0   Max.   : 469.0   Max.   : 332.00   Max.   :292.0   Max.   :0  
   prof_nieve           longitud        latitud           altitud      
 Min.   :   0.0000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:   0.0000   1st Qu.:39.47   1st Qu.: -4.850   1st Qu.:  47.0  
 Median :   0.0000   Median :41.29   Median : -1.411   Median : 287.0  
 Mean   :   0.5838   Mean   :40.09   Mean   : -2.401   Mean   : 484.2  
 3rd Qu.:   0.0000   3rd Qu.:42.19   3rd Qu.:  1.296   3rd Qu.: 690.0  
 Max.   :1240.0000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip      
 Min.   :  1.00   Min.   :-84.0   Min.   :-133.00   Min.   : 453.0  
 1st Qu.: 88.25   1st Qu.: 89.0   1st Qu.:  42.25   1st Qu.: 572.0  
 Median :253.00   Median :138.0   Median :  92.00   Median : 652.0  
 Mean   :202.57   Mean   :135.2   Mean   :  81.52   Mean   : 710.4  
 3rd Qu.:305.00   3rd Qu.:184.0   3rd Qu.: 130.00   3rd Qu.: 791.0  
 Max.   :366.00   Max.   :344.0   Max.   : 240.00   Max.   :1500.0  
     nevada    prof_nieve         longitud        latitud        
 Min.   :0   Min.   :  0.000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.000   1st Qu.:40.82   1st Qu.: -2.3308  
 Median :0   Median :  0.000   Median :41.77   Median :  0.8856  
 Mean   :0   Mean   :  3.178   Mean   :40.96   Mean   : -0.8443  
 3rd Qu.:0   3rd Qu.:  0.000   3rd Qu.:42.38   3rd Qu.:  1.9756  
 Max.   :0   Max.   :820.000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  58.0  
 Median : 261.0  
 Mean   : 667.5  
 3rd Qu.: 953.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 21.0   Min.   : 17.0   Min.   :-41.00   Min.   :1528   Min.   :0  
 1st Qu.: 86.0   1st Qu.: 81.5   1st Qu.: 47.00   1st Qu.:1576   1st Qu.:0  
 Median :286.0   Median :126.0   Median : 90.00   Median :1741   Median :0  
 Mean   :219.8   Mean   :136.5   Mean   : 91.35   Mean   :1820   Mean   :0  
 3rd Qu.:306.0   3rd Qu.:194.5   3rd Qu.:146.50   3rd Qu.:2036   3rd Qu.:0  
 Max.   :333.0   Max.   :252.0   Max.   :192.00   Max.   :3361   Max.   :0  
   prof_nieve    longitud        latitud            altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:0    1st Qu.:39.48   1st Qu.:  0.3181   1st Qu.:  69.0  
 Median :0    Median :40.80   Median :  0.4731   Median : 333.0  
 Mean   :0    Mean   :39.48   Mean   : -1.2462   Mean   : 627.3  
 3rd Qu.:0    3rd Qu.:41.72   3rd Qu.:  1.6841   3rd Qu.:1055.0  
 Max.   :0    Max.   :43.36   Max.   :  2.4378   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax             tmin            precip          nevada 
 Min.   :  1.0   Min.   :-124.0   Min.   :-200.0   Min.   :195.0   Min.   :0  
 1st Qu.: 99.0   1st Qu.: 108.0   1st Qu.:  52.0   1st Qu.:257.0   1st Qu.:0  
 Median :245.0   Median : 146.0   Median :  89.0   Median :300.0   Median :0  
 Mean   :207.2   Mean   : 146.5   Mean   :  83.4   Mean   :317.5   Mean   :0  
 3rd Qu.:307.0   3rd Qu.: 188.0   3rd Qu.: 124.0   3rd Qu.:368.0   3rd Qu.:0  
 Max.   :366.0   Max.   : 372.0   Max.   : 254.0   Max.   :548.0   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :   0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:   0.000   1st Qu.:40.82   1st Qu.: -5.5975   1st Qu.:  61.0  
 Median :   0.000   Median :42.05   Median :  0.0714   Median : 264.0  
 Mean   :   2.664   Mean   :41.20   Mean   : -1.9394   Mean   : 583.8  
 3rd Qu.:   0.000   3rd Qu.:42.56   3rd Qu.:  1.6331   3rd Qu.: 816.0  
 Max.   :1001.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

Visualización de 5 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=5)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=5)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip      
 Min.   :  1.0   Min.   :-196.0   Min.   :-252.00   Min.   :  0.00  
 1st Qu.: 63.0   1st Qu.: 125.0   1st Qu.:  26.00   1st Qu.:  0.00  
 Median :126.0   Median : 167.0   Median :  68.00   Median :  0.00  
 Mean   :160.1   Mean   : 164.4   Mean   :  65.97   Mean   : 15.34  
 3rd Qu.:287.0   3rd Qu.: 211.0   3rd Qu.: 106.00   3rd Qu.:  5.00  
 Max.   :366.0   Max.   : 395.0   Max.   : 264.00   Max.   :292.00  
     nevada    prof_nieve           longitud        latitud       
 Min.   :0   Min.   :   0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.0000   1st Qu.:39.95   1st Qu.: -4.767  
 Median :0   Median :   0.0000   Median :41.48   Median : -1.411  
 Mean   :0   Mean   :   0.8436   Mean   :40.43   Mean   : -2.238  
 3rd Qu.:0   3rd Qu.:   0.0000   3rd Qu.:42.34   3rd Qu.:  1.331  
 Max.   :0   Max.   :1240.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  61.0  
 Median : 370.0  
 Mean   : 552.6  
 3rd Qu.: 790.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip      
 Min.   :  1.00   Min.   :-84.0   Min.   :-133.00   Min.   : 453.0  
 1st Qu.: 88.25   1st Qu.: 89.0   1st Qu.:  42.25   1st Qu.: 572.0  
 Median :253.00   Median :138.0   Median :  92.00   Median : 652.0  
 Mean   :202.57   Mean   :135.2   Mean   :  81.52   Mean   : 710.4  
 3rd Qu.:305.00   3rd Qu.:184.0   3rd Qu.: 130.00   3rd Qu.: 791.0  
 Max.   :366.00   Max.   :344.0   Max.   : 240.00   Max.   :1500.0  
     nevada    prof_nieve         longitud        latitud        
 Min.   :0   Min.   :  0.000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.000   1st Qu.:40.82   1st Qu.: -2.3308  
 Median :0   Median :  0.000   Median :41.77   Median :  0.8856  
 Mean   :0   Mean   :  3.178   Mean   :40.96   Mean   : -0.8443  
 3rd Qu.:0   3rd Qu.:  0.000   3rd Qu.:42.38   3rd Qu.:  1.9756  
 Max.   :0   Max.   :820.000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  58.0  
 Median : 261.0  
 Mean   : 667.5  
 3rd Qu.: 953.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 21.0   Min.   : 17.0   Min.   :-41.00   Min.   :1528   Min.   :0  
 1st Qu.: 86.0   1st Qu.: 81.5   1st Qu.: 47.00   1st Qu.:1576   1st Qu.:0  
 Median :286.0   Median :126.0   Median : 90.00   Median :1741   Median :0  
 Mean   :219.8   Mean   :136.5   Mean   : 91.35   Mean   :1820   Mean   :0  
 3rd Qu.:306.0   3rd Qu.:194.5   3rd Qu.:146.50   3rd Qu.:2036   3rd Qu.:0  
 Max.   :333.0   Max.   :252.0   Max.   :192.00   Max.   :3361   Max.   :0  
   prof_nieve    longitud        latitud            altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:0    1st Qu.:39.48   1st Qu.:  0.3181   1st Qu.:  69.0  
 Median :0    Median :40.80   Median :  0.4731   Median : 333.0  
 Mean   :0    Mean   :39.48   Mean   : -1.2462   Mean   : 627.3  
 3rd Qu.:0    3rd Qu.:41.72   3rd Qu.:  1.6841   3rd Qu.:1055.0  
 Max.   :0    Max.   :43.36   Max.   :  2.4378   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax             tmin            precip          nevada 
 Min.   :  1.0   Min.   :-124.0   Min.   :-200.0   Min.   :195.0   Min.   :0  
 1st Qu.: 99.0   1st Qu.: 108.0   1st Qu.:  52.0   1st Qu.:257.0   1st Qu.:0  
 Median :245.0   Median : 146.0   Median :  89.0   Median :300.0   Median :0  
 Mean   :207.2   Mean   : 146.5   Mean   :  83.4   Mean   :317.5   Mean   :0  
 3rd Qu.:307.0   3rd Qu.: 188.0   3rd Qu.: 124.0   3rd Qu.:368.0   3rd Qu.:0  
 Max.   :366.0   Max.   : 372.0   Max.   : 254.0   Max.   :548.0   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :   0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:   0.000   1st Qu.:40.82   1st Qu.: -5.5975   1st Qu.:  61.0  
 Median :   0.000   Median :42.05   Median :  0.0714   Median : 264.0  
 Mean   :   2.664   Mean   :41.20   Mean   : -1.9394   Mean   : 583.8  
 3rd Qu.:   0.000   3rd Qu.:42.56   3rd Qu.:  1.6331   3rd Qu.: 816.0  
 Max.   :1001.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax            tmin         precip            nevada 
 Min.   : 37.0   Min.   :152.0   Min.   : 40   Min.   :  0.000   Min.   :0  
 1st Qu.:198.0   1st Qu.:257.0   1st Qu.:139   1st Qu.:  0.000   1st Qu.:0  
 Median :231.0   Median :290.0   Median :169   Median :  0.000   Median :0  
 Mean   :232.3   Mean   :287.8   Mean   :169   Mean   :  1.619   Mean   :0  
 3rd Qu.:265.0   3rd Qu.:319.0   3rd Qu.:199   3rd Qu.:  0.000   3rd Qu.:0  
 Max.   :366.0   Max.   :469.0   Max.   :332   Max.   :109.000   Max.   :0  
   prof_nieve          longitud        latitud           altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.98   1st Qu.: -5.498   1st Qu.:  34.0  
 Median : 0.00000   Median :40.82   Median : -1.636   Median : 147.0  
 Mean   : 0.00189   Mean   :39.33   Mean   : -2.764   Mean   : 330.9  
 3rd Qu.: 0.00000   3rd Qu.:41.68   3rd Qu.:  1.168   3rd Qu.: 582.0  
 Max.   :89.00000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

Visualización de 6 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=6)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=6)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip      
 Min.   :  1.0   Min.   :-158.0   Min.   :-240.00   Min.   :  0.00  
 1st Qu.:286.0   1st Qu.: 114.0   1st Qu.:  23.00   1st Qu.:  0.00  
 Median :320.0   Median : 152.0   Median :  60.00   Median :  0.00  
 Mean   :298.3   Mean   : 150.8   Mean   :  56.67   Mean   : 31.01  
 3rd Qu.:343.0   3rd Qu.: 189.0   3rd Qu.:  91.00   3rd Qu.: 38.00  
 Max.   :366.0   Max.   : 395.0   Max.   : 264.00   Max.   :292.00  
     nevada    prof_nieve           longitud        latitud       
 Min.   :0   Min.   :   0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.0000   1st Qu.:40.66   1st Qu.: -4.049  
 Median :0   Median :   0.0000   Median :41.63   Median : -1.117  
 Mean   :0   Mean   :   0.4337   Mean   :41.01   Mean   : -1.660  
 3rd Qu.:0   3rd Qu.:   0.0000   3rd Qu.:42.38   3rd Qu.:  1.401  
 Max.   :0   Max.   :1240.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  91.0  
 Median : 507.0  
 Mean   : 620.9  
 3rd Qu.: 852.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip      
 Min.   :  1.00   Min.   :-84.0   Min.   :-133.00   Min.   : 453.0  
 1st Qu.: 88.25   1st Qu.: 89.0   1st Qu.:  42.25   1st Qu.: 572.0  
 Median :253.00   Median :138.0   Median :  92.00   Median : 652.0  
 Mean   :202.57   Mean   :135.2   Mean   :  81.52   Mean   : 710.4  
 3rd Qu.:305.00   3rd Qu.:184.0   3rd Qu.: 130.00   3rd Qu.: 791.0  
 Max.   :366.00   Max.   :344.0   Max.   : 240.00   Max.   :1500.0  
     nevada    prof_nieve         longitud        latitud        
 Min.   :0   Min.   :  0.000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.000   1st Qu.:40.82   1st Qu.: -2.3308  
 Median :0   Median :  0.000   Median :41.77   Median :  0.8856  
 Mean   :0   Mean   :  3.178   Mean   :40.96   Mean   : -0.8443  
 3rd Qu.:0   3rd Qu.:  0.000   3rd Qu.:42.38   3rd Qu.:  1.9756  
 Max.   :0   Max.   :820.000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  58.0  
 Median : 261.0  
 Mean   : 667.5  
 3rd Qu.: 953.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 21.0   Min.   : 17.0   Min.   :-41.00   Min.   :1528   Min.   :0  
 1st Qu.: 86.0   1st Qu.: 81.5   1st Qu.: 47.00   1st Qu.:1576   1st Qu.:0  
 Median :286.0   Median :126.0   Median : 90.00   Median :1741   Median :0  
 Mean   :219.8   Mean   :136.5   Mean   : 91.35   Mean   :1820   Mean   :0  
 3rd Qu.:306.0   3rd Qu.:194.5   3rd Qu.:146.50   3rd Qu.:2036   3rd Qu.:0  
 Max.   :333.0   Max.   :252.0   Max.   :192.00   Max.   :3361   Max.   :0  
   prof_nieve    longitud        latitud            altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:0    1st Qu.:39.48   1st Qu.:  0.3181   1st Qu.:  69.0  
 Median :0    Median :40.80   Median :  0.4731   Median : 333.0  
 Mean   :0    Mean   :39.48   Mean   : -1.2462   Mean   : 627.3  
 3rd Qu.:0    3rd Qu.:41.72   3rd Qu.:  1.6841   3rd Qu.:1055.0  
 Max.   :0    Max.   :43.36   Max.   :  2.4378   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax             tmin            precip          nevada 
 Min.   :  1.0   Min.   :-124.0   Min.   :-200.0   Min.   :195.0   Min.   :0  
 1st Qu.: 99.0   1st Qu.: 108.0   1st Qu.:  52.0   1st Qu.:257.0   1st Qu.:0  
 Median :245.0   Median : 146.0   Median :  89.0   Median :300.0   Median :0  
 Mean   :207.2   Mean   : 146.5   Mean   :  83.4   Mean   :317.5   Mean   :0  
 3rd Qu.:307.0   3rd Qu.: 188.0   3rd Qu.: 124.0   3rd Qu.:368.0   3rd Qu.:0  
 Max.   :366.0   Max.   : 372.0   Max.   : 254.0   Max.   :548.0   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :   0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:   0.000   1st Qu.:40.82   1st Qu.: -5.5975   1st Qu.:  61.0  
 Median :   0.000   Median :42.05   Median :  0.0714   Median : 264.0  
 Mean   :   2.664   Mean   :41.20   Mean   : -1.9394   Mean   : 583.8  
 3rd Qu.:   0.000   3rd Qu.:42.56   3rd Qu.:  1.6331   3rd Qu.: 816.0  
 Max.   :1001.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax             tmin             precip       
 Min.   :  1.00   Min.   :-196.0   Min.   :-252.00   Min.   :  0.000  
 1st Qu.: 43.00   1st Qu.: 132.0   1st Qu.:  28.00   1st Qu.:  0.000  
 Median : 87.00   Median : 176.0   Median :  73.00   Median :  0.000  
 Mean   : 90.02   Mean   : 171.2   Mean   :  70.69   Mean   :  7.394  
 3rd Qu.:131.00   3rd Qu.: 220.0   3rd Qu.: 115.00   3rd Qu.:  2.000  
 Max.   :259.00   Max.   : 349.0   Max.   : 228.00   Max.   :218.000  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :   0.000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:39.48   1st Qu.: -5.346  
 Median :0   Median :   0.000   Median :41.38   Median : -1.650  
 Mean   :0   Mean   :   1.051   Mean   :40.13   Mean   : -2.531  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.29   3rd Qu.:  1.272  
 Max.   :0   Max.   :1209.000   Max.   :43.57   Max.   :  4.216  
    altitud    
 Min.   :   1  
 1st Qu.:  47  
 Median : 333  
 Mean   : 518  
 3rd Qu.: 775  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt          tmax            tmin         precip            nevada 
 Min.   : 37.0   Min.   :152.0   Min.   : 40   Min.   :  0.000   Min.   :0  
 1st Qu.:198.0   1st Qu.:257.0   1st Qu.:139   1st Qu.:  0.000   1st Qu.:0  
 Median :231.0   Median :290.0   Median :169   Median :  0.000   Median :0  
 Mean   :232.3   Mean   :287.8   Mean   :169   Mean   :  1.619   Mean   :0  
 3rd Qu.:265.0   3rd Qu.:319.0   3rd Qu.:199   3rd Qu.:  0.000   3rd Qu.:0  
 Max.   :366.0   Max.   :469.0   Max.   :332   Max.   :109.000   Max.   :0  
   prof_nieve          longitud        latitud           altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.98   1st Qu.: -5.498   1st Qu.:  34.0  
 Median : 0.00000   Median :40.82   Median : -1.636   Median : 147.0  
 Mean   : 0.00189   Mean   :39.33   Mean   : -2.764   Mean   : 330.9  
 3rd Qu.: 0.00000   3rd Qu.:41.68   3rd Qu.:  1.168   3rd Qu.: 582.0  
 Max.   :89.00000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

Visualización de 8 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=8)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=8)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip      
 Min.   :  1.0   Min.   :-158.0   Min.   :-240.00   Min.   :  0.00  
 1st Qu.:286.0   1st Qu.: 114.0   1st Qu.:  23.00   1st Qu.:  0.00  
 Median :320.0   Median : 152.0   Median :  60.00   Median :  0.00  
 Mean   :298.3   Mean   : 150.8   Mean   :  56.67   Mean   : 31.01  
 3rd Qu.:343.0   3rd Qu.: 189.0   3rd Qu.:  91.00   3rd Qu.: 38.00  
 Max.   :366.0   Max.   : 395.0   Max.   : 264.00   Max.   :292.00  
     nevada    prof_nieve           longitud        latitud       
 Min.   :0   Min.   :   0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.0000   1st Qu.:40.66   1st Qu.: -4.049  
 Median :0   Median :   0.0000   Median :41.63   Median : -1.117  
 Mean   :0   Mean   :   0.4337   Mean   :41.01   Mean   : -1.660  
 3rd Qu.:0   3rd Qu.:   0.0000   3rd Qu.:42.38   3rd Qu.:  1.401  
 Max.   :0   Max.   :1240.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  91.0  
 Median : 507.0  
 Mean   : 620.9  
 3rd Qu.: 852.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax            tmin             precip           nevada 
 Min.   :  1.0   Min.   :-84.0   Min.   :-133.00   Min.   : 453.0   Min.   :0  
 1st Qu.: 88.5   1st Qu.: 89.0   1st Qu.:  41.00   1st Qu.: 567.0   1st Qu.:0  
 Median :246.0   Median :138.0   Median :  91.00   Median : 636.0   Median :0  
 Mean   :200.5   Mean   :135.0   Mean   :  80.63   Mean   : 666.1   Mean   :0  
 3rd Qu.:304.5   3rd Qu.:183.5   3rd Qu.: 129.00   3rd Qu.: 746.0   3rd Qu.:0  
 Max.   :366.0   Max.   :344.0   Max.   : 240.00   Max.   :1014.0   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.87   1st Qu.: -2.6081   1st Qu.:  59.0  
 Median :  0.000   Median :41.84   Median :  0.9844   Median : 261.0  
 Mean   :  2.972   Mean   :41.05   Mean   : -0.8301   Mean   : 669.5  
 3rd Qu.:  0.000   3rd Qu.:42.40   3rd Qu.:  1.9756   3rd Qu.: 953.0  
 Max.   :820.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 21.0   Min.   : 17.0   Min.   :-41.00   Min.   :1528   Min.   :0  
 1st Qu.: 86.0   1st Qu.: 81.5   1st Qu.: 47.00   1st Qu.:1576   1st Qu.:0  
 Median :286.0   Median :126.0   Median : 90.00   Median :1741   Median :0  
 Mean   :219.8   Mean   :136.5   Mean   : 91.35   Mean   :1820   Mean   :0  
 3rd Qu.:306.0   3rd Qu.:194.5   3rd Qu.:146.50   3rd Qu.:2036   3rd Qu.:0  
 Max.   :333.0   Max.   :252.0   Max.   :192.00   Max.   :3361   Max.   :0  
   prof_nieve    longitud        latitud            altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:0    1st Qu.:39.48   1st Qu.:  0.3181   1st Qu.:  69.0  
 Median :0    Median :40.80   Median :  0.4731   Median : 333.0  
 Mean   :0    Mean   :39.48   Mean   : -1.2462   Mean   : 627.3  
 3rd Qu.:0    3rd Qu.:41.72   3rd Qu.:  1.6841   3rd Qu.:1055.0  
 Max.   :0    Max.   :43.36   Max.   :  2.4378   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax             tmin            precip          nevada 
 Min.   :  1.0   Min.   :-124.0   Min.   :-200.0   Min.   :195.0   Min.   :0  
 1st Qu.: 99.0   1st Qu.: 108.0   1st Qu.:  52.0   1st Qu.:257.0   1st Qu.:0  
 Median :245.0   Median : 146.0   Median :  89.0   Median :300.0   Median :0  
 Mean   :207.2   Mean   : 146.5   Mean   :  83.4   Mean   :317.5   Mean   :0  
 3rd Qu.:307.0   3rd Qu.: 188.0   3rd Qu.: 124.0   3rd Qu.:368.0   3rd Qu.:0  
 Max.   :366.0   Max.   : 372.0   Max.   : 254.0   Max.   :548.0   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :   0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:   0.000   1st Qu.:40.82   1st Qu.: -5.5975   1st Qu.:  61.0  
 Median :   0.000   Median :42.05   Median :  0.0714   Median : 264.0  
 Mean   :   2.664   Mean   :41.20   Mean   : -1.9394   Mean   : 583.8  
 3rd Qu.:   0.000   3rd Qu.:42.56   3rd Qu.:  1.6331   3rd Qu.: 816.0  
 Max.   :1001.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax              tmin             precip      
 Min.   :  1.00   Min.   :-196.00   Min.   :-252.00   Min.   :  0.00  
 1st Qu.: 19.00   1st Qu.:  44.00   1st Qu.: -38.00   1st Qu.:  0.00  
 Median : 39.00   Median :  77.00   Median : -12.00   Median :  0.00  
 Mean   : 46.21   Mean   :  69.01   Mean   : -19.73   Mean   : 15.18  
 3rd Qu.: 66.00   3rd Qu.: 104.00   3rd Qu.:   5.00   3rd Qu.: 12.00  
 Max.   :198.00   Max.   : 182.00   Max.   :  62.00   Max.   :218.00  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :   0.000   Min.   :28.31   Min.   :-16.499  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:40.95   1st Qu.: -3.723  
 Median :0   Median :   0.000   Median :41.98   Median :  0.595  
 Mean   :0   Mean   :   5.931   Mean   :41.50   Mean   : -1.160  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.47   3rd Qu.:  1.433  
 Max.   :0   Max.   :1199.000   Max.   :43.57   Max.   :  4.216  
    altitud    
 Min.   :   1  
 1st Qu.: 534  
 Median : 873  
 Mean   :1092  
 3rd Qu.:1894  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 19.0   Min.   :-28.0   Min.   :-70.00   Min.   :1013   Min.   :0  
 1st Qu.: 89.0   1st Qu.: 88.0   1st Qu.: 49.00   1st Qu.:1078   1st Qu.:0  
 Median :284.0   Median :144.0   Median :104.00   Median :1153   Median :0  
 Mean   :224.8   Mean   :138.3   Mean   : 91.03   Mean   :1187   Mean   :0  
 3rd Qu.:308.5   3rd Qu.:188.0   3rd Qu.:135.50   3rd Qu.:1294   3rd Qu.:0  
 Max.   :361.0   Max.   :282.0   Max.   :213.00   Max.   :1500   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :28.05   Min.   :-17.7550   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:39.95   1st Qu.: -1.1993   1st Qu.:  35.0  
 Median :  0.000   Median :41.17   Median :  0.8031   Median : 251.0  
 Mean   :  5.393   Mean   :40.07   Mean   : -0.9964   Mean   : 646.4  
 3rd Qu.:  0.000   3rd Qu.:42.19   3rd Qu.:  2.3642   3rd Qu.:1055.0  
 Max.   :559.000   Max.   :43.57   Max.   :  3.1817   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt           tmax            tmin             precip      
 Min.   :  1.00   Min.   : 38.0   Min.   :-100.00   Min.   : 0.000  
 1st Qu.: 55.00   1st Qu.:155.0   1st Qu.:  52.00   1st Qu.: 0.000  
 Median : 98.00   Median :190.0   Median :  86.00   Median : 0.000  
 Mean   : 98.53   Mean   :191.1   Mean   :  88.24   Mean   : 5.882  
 3rd Qu.:138.00   3rd Qu.:228.0   3rd Qu.: 124.00   3rd Qu.: 1.000  
 Max.   :259.00   Max.   :349.0   Max.   : 228.00   Max.   :99.000  
     nevada    prof_nieve           longitud        latitud       
 Min.   :0   Min.   :   0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:   0.0000   1st Qu.:38.88   1st Qu.: -5.616  
 Median :0   Median :   0.0000   Median :41.17   Median : -1.863  
 Mean   :0   Mean   :   0.1037   Mean   :39.86   Mean   : -2.798  
 3rd Qu.:0   3rd Qu.:   0.0000   3rd Qu.:42.22   3rd Qu.:  1.179  
 Max.   :0   Max.   :1209.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  35.0  
 Median : 185.0  
 Mean   : 406.6  
 3rd Qu.: 632.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt          tmax            tmin         precip            nevada 
 Min.   : 37.0   Min.   :152.0   Min.   : 40   Min.   :  0.000   Min.   :0  
 1st Qu.:198.0   1st Qu.:257.0   1st Qu.:139   1st Qu.:  0.000   1st Qu.:0  
 Median :231.0   Median :290.0   Median :169   Median :  0.000   Median :0  
 Mean   :232.3   Mean   :287.8   Mean   :169   Mean   :  1.619   Mean   :0  
 3rd Qu.:265.0   3rd Qu.:319.0   3rd Qu.:199   3rd Qu.:  0.000   3rd Qu.:0  
 Max.   :366.0   Max.   :469.0   Max.   :332   Max.   :109.000   Max.   :0  
   prof_nieve          longitud        latitud           altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.98   1st Qu.: -5.498   1st Qu.:  34.0  
 Median : 0.00000   Median :40.82   Median : -1.636   Median : 147.0  
 Mean   : 0.00189   Mean   :39.33   Mean   : -2.764   Mean   : 330.9  
 3rd Qu.: 0.00000   3rd Qu.:41.68   3rd Qu.:  1.168   3rd Qu.: 582.0  
 Max.   :89.00000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

Visualización de 10 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=10)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=10)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)
  df.cluster09 <- subset(df, cluster==9)
  df.cluster10 <- subset(df, cluster==10)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax             tmin             precip       
 Min.   :194.0   Min.   :-158.0   Min.   :-240.00   Min.   :  0.000  
 1st Qu.:304.0   1st Qu.: 110.0   1st Qu.:  14.00   1st Qu.:  0.000  
 Median :328.0   Median : 149.0   Median :  50.00   Median :  0.000  
 Mean   :322.8   Mean   : 144.8   Mean   :  44.22   Mean   :  3.301  
 3rd Qu.:347.0   3rd Qu.: 182.0   3rd Qu.:  80.00   3rd Qu.:  0.000  
 Max.   :366.0   Max.   : 329.0   Max.   : 156.00   Max.   :153.000  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :   0.000   Min.   :28.05   Min.   :-17.7550  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:40.66   1st Qu.: -3.8314  
 Median :0   Median :   0.000   Median :41.60   Median :  0.3056  
 Mean   :0   Mean   :   0.426   Mean   :40.97   Mean   : -1.4384  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.33   3rd Qu.:  1.4328  
 Max.   :0   Max.   :1240.000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.: 112.0  
 Median : 534.0  
 Mean   : 658.3  
 3rd Qu.: 890.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin             precip        nevada 
 Min.   :  1.0   Min.   :-104.0   Min.   :-157.00   Min.   : 33   Min.   :0  
 1st Qu.:129.0   1st Qu.: 125.0   1st Qu.:  62.00   1st Qu.: 79   1st Qu.:0  
 Median :250.0   Median : 166.0   Median :  98.00   Median :114   Median :0  
 Mean   :219.4   Mean   : 170.2   Mean   :  96.67   Mean   :120   Mean   :0  
 3rd Qu.:308.0   3rd Qu.: 215.0   3rd Qu.: 135.00   3rd Qu.:155   3rd Qu.:0  
 Max.   :366.0   Max.   : 395.0   Max.   : 264.00   Max.   :292   Max.   :0  
   prof_nieve           longitud        latitud           altitud      
 Min.   :   0.0000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:   0.0000   1st Qu.:40.70   1st Qu.: -5.616   1st Qu.:  69.0  
 Median :   0.0000   Median :41.83   Median : -2.039   Median : 336.0  
 Mean   :   0.4585   Mean   :41.14   Mean   : -2.374   Mean   : 500.5  
 3rd Qu.:   0.0000   3rd Qu.:42.59   3rd Qu.:  1.272   3rd Qu.: 691.0  
 Max.   :1001.0000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin             precip           nevada 
 Min.   :  1.0   Min.   :-84.0   Min.   :-133.00   Min.   : 453.0   Min.   :0  
 1st Qu.: 88.5   1st Qu.: 89.0   1st Qu.:  41.00   1st Qu.: 567.0   1st Qu.:0  
 Median :246.0   Median :138.0   Median :  91.00   Median : 636.0   Median :0  
 Mean   :200.5   Mean   :135.0   Mean   :  80.63   Mean   : 666.1   Mean   :0  
 3rd Qu.:304.5   3rd Qu.:183.5   3rd Qu.: 129.00   3rd Qu.: 746.0   3rd Qu.:0  
 Max.   :366.0   Max.   :344.0   Max.   : 240.00   Max.   :1014.0   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.87   1st Qu.: -2.6081   1st Qu.:  59.0  
 Median :  0.000   Median :41.84   Median :  0.9844   Median : 261.0  
 Mean   :  2.972   Mean   :41.05   Mean   : -0.8301   Mean   : 669.5  
 3rd Qu.:  0.000   3rd Qu.:42.40   3rd Qu.:  1.9756   3rd Qu.: 953.0  
 Max.   :820.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 21.0   Min.   : 17.0   Min.   :-41.00   Min.   :1528   Min.   :0  
 1st Qu.: 86.0   1st Qu.: 81.5   1st Qu.: 47.00   1st Qu.:1576   1st Qu.:0  
 Median :286.0   Median :126.0   Median : 90.00   Median :1741   Median :0  
 Mean   :219.8   Mean   :136.5   Mean   : 91.35   Mean   :1820   Mean   :0  
 3rd Qu.:306.0   3rd Qu.:194.5   3rd Qu.:146.50   3rd Qu.:2036   3rd Qu.:0  
 Max.   :333.0   Max.   :252.0   Max.   :192.00   Max.   :3361   Max.   :0  
   prof_nieve    longitud        latitud            altitud      
 Min.   :0    Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:0    1st Qu.:39.48   1st Qu.:  0.3181   1st Qu.:  69.0  
 Median :0    Median :40.80   Median :  0.4731   Median : 333.0  
 Mean   :0    Mean   :39.48   Mean   : -1.2462   Mean   : 627.3  
 3rd Qu.:0    3rd Qu.:41.72   3rd Qu.:  1.6841   3rd Qu.:1055.0  
 Max.   :0    Max.   :43.36   Max.   :  2.4378   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax             tmin            precip          nevada 
 Min.   :  1.0   Min.   :-124.0   Min.   :-200.0   Min.   :195.0   Min.   :0  
 1st Qu.: 99.0   1st Qu.: 108.0   1st Qu.:  52.0   1st Qu.:257.0   1st Qu.:0  
 Median :245.0   Median : 146.0   Median :  89.0   Median :300.0   Median :0  
 Mean   :207.2   Mean   : 146.5   Mean   :  83.4   Mean   :317.5   Mean   :0  
 3rd Qu.:307.0   3rd Qu.: 188.0   3rd Qu.: 124.0   3rd Qu.:368.0   3rd Qu.:0  
 Max.   :366.0   Max.   : 372.0   Max.   : 254.0   Max.   :548.0   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :   0.000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:   0.000   1st Qu.:40.82   1st Qu.: -5.5975   1st Qu.:  61.0  
 Median :   0.000   Median :42.05   Median :  0.0714   Median : 264.0  
 Mean   :   2.664   Mean   :41.20   Mean   : -1.9394   Mean   : 583.8  
 3rd Qu.:   0.000   3rd Qu.:42.56   3rd Qu.:  1.6331   3rd Qu.: 816.0  
 Max.   :1001.000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt           tmax              tmin             precip      
 Min.   :  1.00   Min.   :-196.00   Min.   :-252.00   Min.   :  0.00  
 1st Qu.: 19.00   1st Qu.:  44.00   1st Qu.: -38.00   1st Qu.:  0.00  
 Median : 39.00   Median :  77.00   Median : -12.00   Median :  0.00  
 Mean   : 46.21   Mean   :  69.01   Mean   : -19.73   Mean   : 15.18  
 3rd Qu.: 66.00   3rd Qu.: 104.00   3rd Qu.:   5.00   3rd Qu.: 12.00  
 Max.   :198.00   Max.   : 182.00   Max.   :  62.00   Max.   :218.00  
     nevada    prof_nieve          longitud        latitud       
 Min.   :0   Min.   :   0.000   Min.   :28.31   Min.   :-16.499  
 1st Qu.:0   1st Qu.:   0.000   1st Qu.:40.95   1st Qu.: -3.723  
 Median :0   Median :   0.000   Median :41.98   Median :  0.595  
 Mean   :0   Mean   :   5.931   Mean   :41.50   Mean   : -1.160  
 3rd Qu.:0   3rd Qu.:   0.000   3rd Qu.:42.47   3rd Qu.:  1.433  
 Max.   :0   Max.   :1199.000   Max.   :43.57   Max.   :  4.216  
    altitud    
 Min.   :   1  
 1st Qu.: 534  
 Median : 873  
 Mean   :1092  
 3rd Qu.:1894  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt          tmax            tmin            precip         nevada 
 Min.   : 19.0   Min.   :-28.0   Min.   :-70.00   Min.   :1013   Min.   :0  
 1st Qu.: 89.0   1st Qu.: 88.0   1st Qu.: 49.00   1st Qu.:1078   1st Qu.:0  
 Median :284.0   Median :144.0   Median :104.00   Median :1153   Median :0  
 Mean   :224.8   Mean   :138.3   Mean   : 91.03   Mean   :1187   Mean   :0  
 3rd Qu.:308.5   3rd Qu.:188.0   3rd Qu.:135.50   3rd Qu.:1294   3rd Qu.:0  
 Max.   :361.0   Max.   :282.0   Max.   :213.00   Max.   :1500   Max.   :0  
   prof_nieve         longitud        latitud            altitud      
 Min.   :  0.000   Min.   :28.05   Min.   :-17.7550   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:39.95   1st Qu.: -1.1993   1st Qu.:  35.0  
 Median :  0.000   Median :41.17   Median :  0.8031   Median : 251.0  
 Mean   :  5.393   Mean   :40.07   Mean   : -0.9964   Mean   : 646.4  
 3rd Qu.:  0.000   3rd Qu.:42.19   3rd Qu.:  2.3642   3rd Qu.:1055.0  
 Max.   :559.000   Max.   :43.57   Max.   :  3.1817   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt           tmax          tmin             precip          nevada 
 Min.   :  1.00   Min.   : 38   Min.   :-100.00   Min.   : 0.00   Min.   :0  
 1st Qu.: 33.00   1st Qu.:136   1st Qu.:  33.00   1st Qu.: 0.00   1st Qu.:0  
 Median : 62.00   Median :157   Median :  58.00   Median : 0.00   Median :0  
 Mean   : 68.09   Mean   :157   Mean   :  56.16   Mean   : 9.16   Mean   :0  
 3rd Qu.: 92.00   3rd Qu.:177   3rd Qu.:  81.00   3rd Qu.: 5.00   3rd Qu.:0  
 Max.   :251.00   Max.   :302   Max.   : 180.00   Max.   :99.00   Max.   :0  
   prof_nieve           longitud        latitud           altitud      
 Min.   :   0.0000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:   0.0000   1st Qu.:39.85   1st Qu.: -4.680   1st Qu.:  58.0  
 Median :   0.0000   Median :41.38   Median : -1.229   Median : 287.0  
 Mean   :   0.1839   Mean   :40.64   Mean   : -1.902   Mean   : 488.4  
 3rd Qu.:   0.0000   3rd Qu.:42.27   3rd Qu.:  1.363   3rd Qu.: 687.0  
 Max.   :1080.0000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster09)

   fecha_cnt          tmax            tmin         precip            nevada 
 Min.   : 37.0   Min.   :152.0   Min.   : 40   Min.   :  0.000   Min.   :0  
 1st Qu.:198.0   1st Qu.:257.0   1st Qu.:139   1st Qu.:  0.000   1st Qu.:0  
 Median :231.0   Median :290.0   Median :169   Median :  0.000   Median :0  
 Mean   :232.3   Mean   :287.8   Mean   :169   Mean   :  1.619   Mean   :0  
 3rd Qu.:265.0   3rd Qu.:319.0   3rd Qu.:199   3rd Qu.:  0.000   3rd Qu.:0  
 Max.   :366.0   Max.   :469.0   Max.   :332   Max.   :109.000   Max.   :0  
   prof_nieve          longitud        latitud           altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.98   1st Qu.: -5.498   1st Qu.:  34.0  
 Median : 0.00000   Median :40.82   Median : -1.636   Median : 147.0  
 Mean   : 0.00189   Mean   :39.33   Mean   : -2.764   Mean   : 330.9  
 3rd Qu.: 0.00000   3rd Qu.:41.68   3rd Qu.:  1.168   3rd Qu.: 582.0  
 Max.   :89.00000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster10)

   fecha_cnt          tmax            tmin           precip           nevada 
 Min.   :  1.0   Min.   :123.0   Min.   : -6.0   Min.   : 0.000   Min.   :0  
 1st Qu.:107.0   1st Qu.:206.0   1st Qu.: 96.0   1st Qu.: 0.000   1st Qu.:0  
 Median :132.0   Median :228.0   Median :124.0   Median : 0.000   Median :0  
 Mean   :131.7   Mean   :228.2   Mean   :123.2   Mean   : 2.313   Mean   :0  
 3rd Qu.:157.0   3rd Qu.:250.0   3rd Qu.:154.0   3rd Qu.: 0.000   3rd Qu.:0  
 Max.   :259.0   Max.   :349.0   Max.   :228.0   Max.   :93.000   Max.   :0  
   prof_nieve           longitud        latitud            altitud      
 Min.   :   0.0000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:   0.0000   1st Qu.:37.78   1st Qu.: -6.0556   1st Qu.:  32.0  
 Median :   0.0000   Median :40.96   Median : -2.4831   Median :  98.0  
 Mean   :   0.0165   Mean   :39.02   Mean   : -3.7731   Mean   : 317.4  
 3rd Qu.:   0.0000   3rd Qu.:42.08   3rd Qu.:  0.8856   3rd Qu.: 541.0  
 Max.   :1209.0000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.hist(df.cluster09)

if (!empty_nodes) mpr.hist(df.cluster10)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster09)

if (!empty_nodes) mpr.boxplot(df.cluster10)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
  df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
  df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)